Tuesday, June 08, 2010

But I Don't Want to Write about John Tierney Again

Thanks for all the e-mails and comments with links to the New York Times commentary by John Tierney, but what he wrote is just more of the same of what he's written before: i.e., many women don't want to be scientists or engineers, others can't because they aren't as good at math as the guys. Oh yeah, and Larry Summers made some reasonable statements in a speech that was misunderstood by hysterical females.

This is the person who questioned the National Academy of Science's report, Beyond Bias and Barriers, because a committee with lots of women on it produced the report (and women aren't objective about these issues).

This is the person who wrote a bizarre op-ed column about how women who accompany their husbands to Civil War reenactments must find it liberating to wear bulky clothes that make them swelter in the heat. The women don't get to do excellent things like pretend to kill people and be killed (only authentic men can do that), but at least fat women even look kind of good if you bundle them up enough.

And so on. I stopped reading his commentaries after that, until forced under torture to read his latest thoughts on the topic of Women.

His new essay is more of the same: There are flawed studies that show that females and males have similar quantitative skills and better studies that show that more males than females are extremely talented at math. This is one reason why men are more successful in math, science, and engineering. If women were good at math and science, perhaps they would understand these scientific studies with all the numbers in them.

On one point I reluctantly sort of agree with him: i.e., workshops to "enhance gender equality", mandated if certain legislation becomes law, could be kind of grim. In all likelihood, these would be yet another sounds-good-in-theory administrative requirement that PIs and others would have to sit through to be allowed to run our research groups.

I could be wrong about that. I know that some targeted workshops on equality issues -- e.g., for hiring committees or department chairs -- if run by peers with experience in the relevant activities, can be very effective.

I am, however, picturing something along the lines of the dismal "ethics" workshops that researchers at my university have to attend at regular intervals, or a workshop I went to about teaching those delicate creatures known as "first year students" -- a workshop at which we professors were instructed by people who had never in their lives taught a first year, or any, student. I am thinking about other workshops at which participants were bombarded with surveys filled with leading questions that provided data so that someone could assess what we think before and after we are workshopped. I do not play well with others when I am being assessed by poorly worded surveys.

I am all for equality workshops if by some miracle they are more effective than other workshops that help us be better researchers and teachers. I do not agree with Tierney that these workshops are unnecessary because (1) what inequality exists is just the natural order of things (men are better at math), and (2) "careful" studies show that women already get lots of grants and promotions and therefore there is no inequality in those respects (apparently the subject of a future column).

My favorite quotation of the essay, on the topic of high SAT scores: "..someone at the 99.9 level is more likely than someone at the 99.1 level to get a doctorate in science or to win tenure at a top university."

He does not, however, take that thought too far (smart!), and he does admit that there is possible "social bias" against women. And some other complex factors. And stuff. But don't expect a lot of women scientists at top universities anyway.

We have made some progress towards increasing the participation of women in STEM fields in recent years, but an important question that many are asking now is why the greater numbers of women undergraduate and graduate students in STEM fields has not translated into more women at more advanced levels of academia and industry. Something just doesn't add up.

48 comments:

Bagelsan
said...

"..someone at the 99.9 level is more likely than someone at the 99.1 level to get a doctorate in science or to win tenure at a top university."

"Your research is just phenomenal Ms. Brown. Truly groundbreaking. And we would love to sign off on your thesis ...except that I notice you got a mere 1580 on your SATs...? *frowns* We take antiquated high school standardized testing very seriously in this prestigious Ph.D. program, miss."

I'm sure I've made this comment before, but Virginia Valian's excellent book 'Why so slow' assesses the EVIDENCE for gender bias in the professions, based on decades of research in psychology and management. She reports clear evidence that women are disadvantaged because everyone (not just men) has deep-routed schemas about how people from different groups should behave (e.g. women = nurturing, men = leading). These schemas are unconscious, learnt in childhood and very hard to eradicate. Because these schemas are unconscious, the people who say "I'm all for equality" can still show unconscious biases that are much harder to argue against. The best solution seems to be to be aware of the schemas and to apply, at the personal and organisational level, systems to promote equality.

Oh this stuff drives me nuts! I note that he didn't cite the comprehensive meta-analytic review of the evidence for sex differences in science and maths that Halpern et al. published in 2007 (http://www.psychologicalscience.org/journals/pspi/pspi_8_1_article.pdf). Was that because the female authors outnumbered the male?

The article contains many nuanced conclusions but one of the main points is that experience alters brain structure and functioning, so causal statements about male/female brain differences and success in math and science are circular. Instead, they conclude that choice of STEM career is subject to complex additions and interactions between early experience, biological factors, educational policy, and cultural context.

But then, why cite meta-analyses when you can cherry-pick your findings?

This post seems quite germane to me right now as I attend a conference in my field (astrophysics, which is more "female-friendly" than some other physics fields) and sat through 16 talks yesterday, only two of which were from women.

Well, if you have to argue that way we would have come full circle back to the reason that women have to child-bear AND take full responsibility of looking after the children...if only jobs are equally shared across the genders...in ALL levels.

We are talking about 7th gradres getting 700+ on the SAT, which is normally taken by 11th graders.

For a 7th grader to do that well he would have had to be in a curriculum different from the standard one or, much more likely, is being individually taught.

That is, someone in his family is taking the time every day to teach the kid in algebra, geometry, etc. and the kid is putting in several hours every day doing practice problems.

So, it is not just about the kid, its about his family and his peer group (if he chooses to pay attention to what they say).

I was a 12-yo geek boy with no friends, who loved to spend all my time learning assembly language and quantum physics because, I had nothing else to do. My sister was just as smart as me but, being a girl, she had friends, girlfriends. Guess who got a science PhD? Guess who married a banker?

There is a far better response that could be given than an ad hominem attack against Tierney. His argument is inherently flawed.

The variability hypothesis (which shows that the standard deviation, not the mean of aptitude is larger for men) is nearly always ascribed to a biological difference. There is, however, no reason to believe this is so. Just as extremely small biological effects might not show up until you reach +/- three standard deviations, extremely small cultural effects might not show up until you reach +/- three standard deviations.

In other words, even if these studies are correct that variability is very, very slightly smaller for women than for men (a point I wouldn't concede without knowing more about the studies than I do), there is absolutely no reason to think that this very small effect is due to nature. Nurture seems to be just as good a candidate, and more plausible given what we know about how gender differences seem to vanish in many fields when they once seemed an eternal feature of the job landscape.

Besides, what do most academics do? WRITE grants, WRITE papers, and VERBALly communicate via lectures and seminars. Women test better at verbal skills (not that I'm a fan of the tests). Tierney also left out the studies on how a paper with a woman's name gets lower ratings than the same paper with a man's name. Something tells me there are social factors at play.

Oh yeah, and Larry Summers made some reasonable statements in a speech that was misunderstood by hysterical females.

I have been told that I am too emotional/sensitive or not having a sense of humor a fair number of times when the man is losing an argument, or is getting embarrassed, or I have just called him on being an a**hole and he wants to deny it, or yes I did get angry for the a**holishness.

I don't have a good comeback to someone telling me I'm being emotional. Any comeback that involves "No, I am not," or "No, you are at fault" seems to propagate that I am indeed all that.

All these ad hominem attacks on Tierney aside, his main point seems to be that men's bell curves are wider than women's, and hence there are more outliers on both sides for men. Further, his argument requires this difference be significant enough to skew ratios in the scientific leadership.

The argument seems plausible - certainly men and women have differences in physique, life expectancy, etc.. Certainly men well populate the left side of curves in prisons and behavior in classrooms.

He'd have to concoct a different argument to explain how "the seventh-grade girls outnumbered the boys at the right tail of tests measuring verbal reasoning and writing ability".

Is there a good critical, quantitative overview of his bell-curve argument to which someone can link? Some cause and effect relations are unresolvable, but this should succumb to a little observational science.

@John V (and anyone else wanting a discussion of what the studies in the cognitive differences between men and women actually show)- I recommend you read the book Pink Brain, Blue Brain, by Lise Eliot.

She cites some fascinating studies that always seem to be overlooked by the people claiming that women are just inherently worse at math. I'll give you a spoiler- nurture starts pretty early, and careful studies looking for true biological differences find at most small differences. Dr. Eliot's argument is that we amplify these small differences with how we raise our kids, and that this does a disservice to both boys and girls. It is a very interesting book.

Instead of obsessing about this kind of stuff, science should be thinking about how it can KEEP the women who can compete with the oh so wonderfully smart men, but leave science for OTHER reasons. Yeah, we should do something at all levels, but there are tons of women who have proven their skills at the BS or PhD level who still end up leaving science. Why not focus more on keeping them?

Oh, because "real" scientists if devote 80 hours a week to it and give up on the idea of having a family and a life outside of science. So, women are just "choosing" to not be scientists....AS SCIENTISTS ARE CURRENTLY DEFINED. If you broadened your definitions and made the scientific culture more accepting of all people who love science and are good at it and put in an effort to succeed, I bet you'd catch more flies/women/minorities/men who aren't crazy aspergers.

Sure that is just my opinion from my experience, but tell me you all haven't seen the same thing?

In 7th grade, I received a 700 on the math section of the SAT, and a 650 verbal (I'm a girl). I'm currently pursuing a doctorate in the sciences. By all accounts, I'm one of the success stories. A closer look however, shows the effects of gender, even in my case. My 700 score was basically the best one could do with no knowledge of geometry, which I hadn't had at that point. My male peers all had some knowledge of geometry for one simple reason- they were on the math team and learned it after class. While my SAT scores were higher than theirs, and I beat them all when I joined the team for exactly one day of my middle school career, they had the *potential* for higher scores than myself.

Why didn't I join the math team, despite my exceptional ability? Frankly, the idea of joining 5 adolescent boys by myself wasn't too appealing, as I think the parent of any middle-school boy will understand. Nevertheless, the fact remains that I could have scored much better on the exam if I had been on the team and exposed to geometry. One could even say I had to be *better* than my male peers to achieve the same score.

Looking at the top .01 percentile may seem like a fair way of evaluating ability across the genders, but as my case shows, there are still many factors to control for. When 7th-grade math teams, which would be lucky to have one student at the 99% range, let alone 99.9%, have equal numbers of boys and girls, then maybe the study will be a direct comparison.

JohnV, bell curves for intelligence may be different, but I don't think this explains the lack of women in STEM fields. After all, a wider bell curve may produce more geniuses, but genius is not required to be in science. Usually the men and women in science are above average, but not geniuses. The bell cures may be different at the ends, but this means there should still be similar numbers of men and women of above average intelligence, which is just fine for being in a STEM field. So why aren't the women there?

The smartest thing about the framers of the variability hypothesis is that they framed their poorly-supported and almost certainly wrong idea in such a way that it is often misrepresented, which means that they get to sit back and say "that's not what I said" while feeling (falsely) superior to their detractors. The variability hypothesis isn't that women as a group are worse at math--it is often suggested that women as a group may have a slightly higher mean score, and that with the wider variance for men there will actually be more men on the extreme low end. The part of the variability hypothesis that favors men is on the extreme right end of the bell curve, where a hypothesized wider standard deviation would mean more outliers.

The problems with the variability hypothesis are:1) Supporting evidence is weak at best, as others have pointed out.

2) Even if (for the sake of argument) the hypothesis is true, it doesn't automatically follow that it must be the result of nature rather than nurture, as was pointed out by another commenter.

3) Even if (for the sake of argument) the variability hypothesis is true for the extreme outliers, it is of limited applicability for your typical STEM program. Most students majoring in science and engineering are smart, but they aren't all at the 99th percentile. Your average college science engineering classroom has plenty of kids who are closer to the center of the bell curve, at least by some measures. Yet we still see a gender gap in a room drawn from closer to the center of the bell curve. A hypothesis that focuses on the extreme end cannot explain that. We have to look to environmental factors.

4) Even if (for the sake of argument) the variability hypothesis is true for some particular trait, success at the highest levels of science takes a mix of raw talent for abstract reasoning, skills in hands-on work, creativity, dogged determination, interpersonal skills to navigate the politics of a competitive profession, luck, and a host of other factors. Being at the far end of the bell curve for one particular trait doesn't mean that you'll have the other attributes. So I'd expect the most successful people to get where they are based on a total package of skills rather than super-genius performance by one particular measure that may or may not be distributed in the way that Tierney thinks it's distributed.

Thanks for the Pink/blue brain book tip, I checked the Amazon reviews, just what I wanted, and it's now on my phone to read on this evening's plane trip.

My personal hunch (based on my daughter) is that girls tend to distain inhumane STEM fields in favor of richer interpersonal occupations, rather than much disparity in skill, but many factors are possible.

Steph said: … science should be thinking about how it can KEEP the women who can compete with the oh so wonderfully smart men, but leave science for OTHER reasons. Yeah, we should do something at all levels, but there are tons of women who have proven their skills at the BS or PhD level who still end up leaving science. Why not focus more on keeping them?

I think this is an important point. A disproportionate lack of advancement of women with BS in STEM to PhD and beyond points to a clear bias; this issue is also a very good immediate target for fighting against bias as it is conducive to corrective measures by academic institutions.

In contrast, addressing society-wide stereotypes at the level of individual families and communities is very hard to do directly, and these changes require many generations. Teenage socialization plays a significant role in why some girls simply drop the idea of science in middle and high school, and this issue is difficult to combat because it's at the society's core.

However, if the image of a top-notch scientist changes fairly rapidly (as a result of ensuring we don't lose women who do get science PhDs through focused efforts by academic institutions) and women STEM profs and researchers are no longer extreme rarities, this change has the ability to diffuse into the broader population at all levels and help change the overall perception of women’s role in science.

In a nutshell, I think more focus on correcting the gender bias at the PhD and above levels has the ability to significantly enhance the efforts focused on direct outreach to middle and high-school girls.

@JohnV I think you're falling prey to the "old white man locked alone in a lab" stereotype. In reality, most scientists today need a host of interpersonal skills, as they work with collaborators, advise grad students and post docs, and teach students. As a woman who has worked both in science research and in industry, I can say that I felt more fulfilled both intellectually and on an "interpersonal" level working in the lab than working in a cube.

I'm very sympathetic to the concerns about gender inequity. But I have to admit I was mildly disappointed with this blog post: I felt it was a missed opportunity to patiently identify the holes in Tierney's arguments, one by one (if indeed they are faulty).

I came to this blog hoping to find a substantive response to the claims in Tierney's column. I was hoping folks on this blog could supply me with some ammunition to respond to the people who make these arguments -- if the arguments are faulty. Ad hominem attacks, while satisfying to someone who is already convinced that Tierney is wrong, do not respond to the substance of his arguments. I understand there is a lot of frustration about having to respond to these arguments, but can anyone point me to a reasoned response to the arguments?

Hypothesis: When it comes to math/science ability, the male and female means may be the same, but the male standard deviation may be higher than the female standard deviation.

Here are the responses I've seen on this blog:

Response a) Sure, the hypothesis might be true, but that doesn't prove it is nature instead of nurture. [I agree this is a good point. That said, as good scientists, shouldn't we also acknowledge in the same breath the possibility that nature could play a significant role? Is there strong evidence pointing one way or another? ]

Response b) The hypothesis doesn't seem to explain observed gender inequity, as math/science folks aren't always from the top 1% of people. [Hmm. Well, in my department, yes, I would guess that probably all of our grad students and faculty are likely from the top 1% of the population, in terms of math/science ability. And in any case, it still misses the point. Even if all math/science faculty were drawn from, say, the top 5% of the population in terms of math/science ability, then there could still be significantly more males above that threshold than females, under the hypothesis of same mean but different std dev. Or, to put it another way: to evaluate this response, we need to look at the numbers. Given the alleged means and standard deviations in ability for males and females, and given the median math/science ability of faculty, we can then do the calculus to calculate the relative numbers of males vs females above that threshold, and compare to the actual number of male and female math/sci faculty. So for anyone who wants to respond in this way, I think this response will only be convincing if you can show us the numbers and show us the math.]

I write anonymously because, after the Larry Summers incident, there is no way that I would ever dare say a single word about this subject without anonymity -- this is apparently too sensitive a subject. It's too bad, because I think the best way to get to the truth is to have a robust discussion from all sides, but at least we have forums like this one that are open to intelligent discussion. In any case, I'm very interested in hearing substantive responses to this hypothesis, evidence for or against it, reasons why it should or should not be taken seriously, etc. Thanks for your thoughts!

Anonymous prof,I think the reason why we can't give a well-reasoned, quantitative response to the article is because the argument in the article was not quantitative and provided no metric or predictions which we can attempt to disprove.

Such is the problem with these speculative issues. It's prob also the reason why people shudder at having to read some random guy's opinions on them.

...we need to look at the numbers. Given the alleged means and standard deviations in ability for males and females, ...So for anyone who wants to respond in this way, I think this response will only be convincing if you can show us the numbers and show us the math

Im glad you refer to it as "alleged" means and standard deviations. The signals we are inputing into the model are very noisy and (as much research has shown), highly biased. So like I tell many of my students, you can show me the math... but what does it mean? I think we need to be careful. Bad data is bad data. Differences between individuals seems to have greater variance.

How many of you knew that Florence Nightengale was the first female member of the Royal Statistical Society (1859), and credited with developing polar area diagrams?

Hah. My field often still has 100% male conferences. There are so called legal requirements at some places for PIs to report on gender equality measures. Goodness knows what they write in their reports, because they sure as hell aren't doing anything to help women in the field. Believe me, I know, because I AM the woman in the field. Oh, some of them THINK they are helping ... eg., by introducing you to male colleagues who are desperate to find postdocs to exploit ... problem is that they do this without bothering to read anything that you ever wrote to check whether or not there is, say, some conflict of interest.

It doesn't occur to them to actually ASK you about it either. It's like, "here you are, honey, go work for Max. He's super smart and cool and it will do you good. Never mind your pretty head about ... now what was it you were researching for the last 10 years? Oh honey, it's not important."

AnonProf: you're a professor? Really? And to think I can't even get a postdoc! My research abilities were better than yours when I was 8 years old. OK, then, since you are incapable of doing 5 minutes of intelligent reading on your own ...

IQ SCORES ARE NORMALISED SO THAT THE CURVE IS IDENTICAL FOR MEN AND WOMEN. RESEARCH SHOWS THAT POTENTIAL DIFFERENCES ON SPECIFIC TASKS SUCH AS SPATIAL REASONING (WHICH WOMEN DO BETTER AT IF THEY ARE TAUGHT) ARE SMALL AND DO NOT ACCOUNT FOR THE ENORMOUS MALE FEMALE RATIO IN STEM FIELDS.

Moreover, whatever gave you morons the idea that a super high IQ makes a good STEM professional? In my experience it most certainly does not (and since I am way up there in the tail and a theoretical physicist, my experience is not negligible).

Another thing that should be noted: even Summers never claimed that this "long tail" effect was the only cause of underrepresentation of women in top STEM positions, but that it was one of multiple factors, including work expectations that seem to assume the faculty member has a stay-at-home spouse that does everything (the long hours) and gender bias/discrimination (though Summers thought this was a minor factor, and I think it's a major one). Many, many more could be added (bad parental leave policies and a macho culture in some environments leap to mind as but two).

Personally, I don't put much stock in the long tail hypothesis, but that may be in part because it sounds too much like arguments in other fields that vanished once women achieved parity. But it's not the case that even Summers argued that "women can't do STEM" or "the long tails explain everything". I think everyone agrees that the barriers to equality are complicated and messy, and that we need to keep eroding them.

If the ideas are that wrong (and I think they are), there's no reason to distort them to make them an even juicier target. We can be scrupulously fair to our intellectual opponents and still carry the day with data. Many people here have done exactly that, and that's been too rare in this debate.

Hmm. Well, in my department, yes, I would guess that probably all of our grad students and faculty are likely from the top 1% of the population, in terms of math/science ability.

1) Tierney referenced work on the top 0.01% of mathematical achievement, which is 2 orders of magnitude smaller of a group. We're talking about 1 in 10,000 Americans, or about 32,000 people. Of those 30,000, only about 20,000 are old enough to be in or past college but young enough to not yet be retired.

Let's assume that the top 50 physics and math departments (100 departments total) each have of order 30 faculty and 70 grad students (on average, and keeping to nice round numbers of 100 people per department).

Now consider the upper echelons of academic engineering and computer science and materials science and quantitative biology and other math-intensive fields. And all of the people who got advanced degrees in these fields and went4 into industry. Put them together, and the top tier of math-intensive professionals is easily a few hundred thousand people, or 10x the size of the narrow cohort that Tierney limited his attention to.

So, even if (for the sake of argument) there is evidence of a biologically-based disparity in a certain group, that group is much smaller than the elite tier of math-intensive professionals. A study on a small, special group cannot explain more than a fraction of the entire profession, even if (for the sake of argument) the study is sound.

2) Most people working in STEM did not get advanced degrees from the top 50 departments. They got bachelors degree from a much wider range of schools. No way are they in the top 0.01% or even the top 1%. My understanding is that when you study the top 10% on math tests taken in middle school there is little or no statistically significant gender discrepancy. (I can't find the cite right now.) Despite that, the students in STEM still skew male. Clearly there are factors in play other than mathematical ability.

What bothers me most, is that this discussion seems to be focusing on the problem of women (and some races) in obtaining top positions in academia.

To me, this seems to be just one symptom of a much larger problem. From the AAUW (formerly known as the American Association of University Women),

"As early as one year after graduation, a pay gap is found between women and men who had the same college major. In edu- cation, a female-dominated major, women earn 95 percent as much as their male colleagues earn. In biological sci- ences, a mixed-gender major, women earn only 75 percent as much as men earn. Likewise in mathematics—a male- dominated major—women earn only 76 percent as much as men earn. Female students cannot simply choose a major that will allow them to avoid the pay gap."

Many people attribute this to poor negotiation, or the like. It surely can't be because of academic record, since women tend to have higher GPAs, on average, then men. In all these cases, it is easy to point the finger and blame one thing or another, which can't be easily changed. Eventually, when all else fails, then you just put up your hands and say it is innate. That way, we don't have to think about how to fix the problem.

Anonymous Professor, you wanted evidence for point A: Would cross temporal shifts over time work for you?

Stephen Ceci, Wendy Williams, and Susan Barnett looked at these issues. One interesting thing is Tierney cites the extremes in intelligence as a biological factor, but there is evidence that they aren't fully explained by variables like genes and hormones. For example, among the seventh graders scoring 700 or higher on the SAT-M, there were 12.9 males for every female in the earliest years of the SMPY studies, compared to 2.8 now. The numbers declining before the shifting SAT scoring scale as well.

Key Quote: "We found that evidence for a direct effect of innate hormonaldifferences on math and spatial ability (the basis for the intrinsicability-differences biological model in Figure A2) is contradictoryand inconclusive, with scant data on right-tail samples. Despite thefailure to link sex differences in mathematical and spatial ability toprenatal and postnatal hormones, the fact is that there are persistentsex differences in spatial reasoning and mathematical ability at theright tail, on the order of approximately 2 to 1 on various gatekeepertests such as SAT-M and GRE-Q (e.g., Hyde et al., 2008),which may reflect purely sociocultural factors, purely biologicalfactors, or some combination. On the basis of transnational datashowing very inconsistent sex differences at the right tail, includingcountries where they are absent or even reversed (e.g., Guisoet al., 2008; Penner, in press) and U.S. data showing a narrowingof the sex gap at the right tail over time (Gates, 2006b), weconclude that the bases of mathematical and spatial differences arealmost certainly not purely biological but rather must include astrong sociocultural component."

Posts like this are why I, as a female scientist, often feel alienated from the "women in science" community. Misstating Tierney's and Summers' arguments, scoffing at data if its implications are unacceptable, attacking those like AnonProf who call for open discussion--these are not the actions of people committed to free and open inquiry.

I have to start with your side comment about "a workshop I went to about teaching those delicate creatures known as 'first year students' -- a workshop at which we professors were instructed by people who had never in their lives taught a first year, or any, student." I have a colleague who loves to go to presentations like that. Within 5 minutes his hand shoots up with a question about the presenter's first-hand experience with such students in an algebra class. It is priceless. So was the time that we were given a poorly designed Powerpoint LECTURE about why we shouldn't give lectures, and someone asked why the presenters had given a lecture if that delivery mechanism was so poor. They were speechless.

Tierney writes, about the 700 SAT 7th-grader sample:"In the early 1980s, there were 13 boys for every girl in that group, but by 1991 the gender gap had narrowed to four to one ...." (I am snipping off his explanation of this bit of data because it is his speculation, not data.) If that spectacular change is unlikely to be explained by genetic changes in the population, how do you know that ANY of the remaining differences are? You don't.

But my real question is whether Tierney knows what the symbol "beta" represents in the equation being whispered seductively by the woman in the side graphic. He is, after all, a science writer, not a scientist.

Not necessarily. My son scored 720 on math at the end of 6th grade without "several hours every day". He did math for fun occasionally and had some extra instruction at home (mainly when he bought himself Calculus for Dummies with his birthday money and wanted clarification on some points not covered well there), but not nearly at the level Anonymous implies is needed. He *did* have "non-standard curriculum"---he got the geometry in school (well, mainly teaching himself from the Art of Problem Solving geometry book, but the teachers did grade the homework he assigned himself).

Some kids are just good at math and learn it without intensive instruction and laborious practice.Historically, there have been more boys than girls in that group (still are if you look at the high scorers on high-ceiling tests like AMC-8 or AMC-10), but that could be as easily because of socialization as anything innate. In fact, similar measurements in other countries do get different ratios by gender, making genetic explanations less convincing.

Anon 3:54, one of the things that absolutely infuriates my irrational female mind is that we (and I'm pointing the finger at myself too since I'm bothering to type this) bother to take Tierney seriously even though he has neither the qualifications nor the experience one needs to obligate us to examine his arguments in detail, or really at all. It's like us trying to refute the arguments of a 15 year old quarterback who claims that all women secretly want to be fucked in the ass.

FSP, you should do a post on workshops about teaching first year students, because I'm sure that you and many readers have some stories akin to this one by Dr. Pion:

I have a colleague who loves to go to presentations like that. Within 5 minutes his hand shoots up with a question about the presenter's first-hand experience with such students in an algebra class. It is priceless. So was the time that we were given a poorly designed Powerpoint LECTURE about why we shouldn't give lectures, and someone asked why the presenters had given a lecture if that delivery mechanism was so poor. They were speechless.

My own story is when an education research was lecturing about not lecturing, and one reason he gave was that we need to be strong in science and engineering to be competitive with China and India. So my hand shot up and I asked whether we should teach science the way that China and India do.

On one point I reluctantly sort of agree with him: i.e., workshops to "enhance gender equality", mandated if certain legislation becomes law, could be kind of grim. In all likelihood, these would be yet another sounds-good-in-theory administrative requirement that PIs and others would have to sit through to be allowed to run our research groups.

A nit, but nevertheless I must pick: It is my understanding that the workshops are not "to enhance gender equality" per se, as in teaching people how to play nice, etc. According to the AAUW press release about the America Competes act, the workshops are supposed to "educate program officers, members of grant review panels, and others about methods that minimize the effects of gender bias in evaluation of federal research grants and faculty hiring and tenure practices." In other words, they are supposed to teach people who are operating in decision-making capacities what the relevant research says about how gender bias can operate in those decision-making processes, and what steps one can take to avoid or minimize that bias. "Enhancing gender equality" (or equity, as I prefer) would not be an outcome of the workshop itself - that eventual outcome would depend upon whether the attendees actually paid attention and decided to try using any of the information they were presented with at the workshop.

The information is all currently available, and many universities that have ADVANCE grants have gathered a lot of it and made it readily accessible on their websites. Sadly, faculty search and hiring committees, tenure and promotion committees, program officers, and grant review panels either remain unaware of these resources or choose not to avail themselves of this information.

If you were doing a scientific experiment, and there was information available that would help you minimize artifacts in your setup and data collection, you'd want to know about it, no doubt - you'd do everything you could to get your hands on the information and learn from other colleagues. But when someone wants to offer similar information about minimizing gender bias in your decision making, we roll out the jokes about how wretched workshops are and how they will do no good and we cross our arms and declare that we will make sport and just not learn a damn thing. Yay! We are such independent free-thinking scientists! No damn workshops and surveys for us! Nobody can teach us anything unless they are just like us! And doing what we do! Because we are absolutely sure there's no knowledge outside of our scientific field we need to know! Yay!

It is possible the workshops will be poorly constructed and implemented with a heavy hand. But it is also possible that they will contain useful information that people with willing hearts could take home and use to try and make things just a tiny bit better. It would be great if everybody would spontaneously go on their own and read the stuff on the various websites, but they won't.

"..someone at the 99.9 level is more likely than someone at the 99.1 level to get a doctorate in science or to win tenure at a top university."

Someone at this level on what day? after a single test? There is no such thing as "someone at the 99.9 level" vs. "someone at the 99.1 level," and if there is, the average college search committee can't determine which is which (btw, on what measure is this a statistically significant difference?). When people are at these kinds of levels, the difference is insigificant and undetectable--and the judgments are far more likely to be based on subjective criteria.

About Me

I am a full professor in a physical sciences field at a large research university. I am married and have a teenaged daughter.
I have the greatest job in the world, but this will not stop me from noting some of the more puzzling and stressful aspects of my career as a science professor.
E-mail (can't promise to reply): femalescienceprofessor@gmail.com